Carolin Schmitt

I am a PhD student in the Autonomous Vision Group of Andreas Geiger and scholar as well as student representative of the International Max-Planck Research School for Intelligent Systems (IMPRS-IS). My current research is on reflectance estimation in the area of Computer Vision and Computer Graphics.

Reflections reveal a lot of information on the shape of an object and the material it consists of. Knowledge of it allows better scene understanding, refinement of 3D reconstructions and is needed for rendering virtual scenes. I am working on inferring the Bidirectional Reflectance Distribution Function (BRDF) from RGB video input and on how to use this information for photo-realistic indoor scene reconstructions.

Education

2017 ongoing PhD at the MPI-IS in Tübingen, Autonomous Vision Group
Supervisor: Prof. Dr. Andras Geiger
Scholar of the International Max-Planck Research School for Intelligent Systems

In this paper, we consider the problem of reconstructing a dense 3D model using images captured from different views. Recent methods based on convolutional neural networks (CNN) allow learning the entire task from data. However, they do not incorporate the physics of image formation such as perspective geometry and occlusion. Instead, classical approaches based on Markov Random Fields (MRF) with ray-potentials explicitly model these physical processes, but they cannot cope with large surface appearance variations across different viewpoints. In this paper, we propose RayNet, which combines the strengths of both frameworks. RayNet integrates a CNN that learns view-invariant feature representations with an MRF that explicitly encodes the physics of perspective projection and occlusion. We train RayNet end-to-end using empirical risk minimization. We thoroughly evaluate our approach on challenging real-world datasets and demonstrate its benefits over a piece-wise trained baseline, hand-crafted models as well as other learning-based approaches.

Our goal is to understand the principles of Perception, Action and Learning in autonomous systems that successfully interact with complex environments and to use this understanding to design future systems